English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Capturing both the structural and temporal aspects of interactions is crucialfor many real world datasets like contact between individuals. Using the linkstream formalism to capture the dynamic of the systems, we tackle the issue ofactivity prediction in link streams, that is to say predicting the number oflinks occurring during a given period of time and we present a protocol thattakes advantage of the temporal and structural information contained in thelink stream. Using a supervised learning method, we are able to model thedynamic of our system to improve the prediction. We investigate the behavior ofour algorithm and crucial elements affecting the prediction. By introducingdifferent categories of pair of nodes, we are able to improve the quality aswell as increase the diversity of our prediction.

Predicting interactions between individuals with structural and dynamical information